Introducing a Family of Synthetic Datasets for Research on Bias in Machine Learning
This addresses the problem of limited data availability for researchers studying bias in ML, but it is incremental as it provides a new tool rather than a breakthrough.
The authors tackled the lack of datasets for bias research in machine learning by introducing a family of synthetic datasets, demonstrating their utility through a simple experiment.
A significant impediment to progress in research on bias in machine learning (ML) is the availability of relevant datasets. This situation is unlikely to change much given the sensitivity of such data. For this reason, there is a role for synthetic data in this research. In this short paper, we present one such family of synthetic data sets. We provide an overview of the data, describe how the level of bias can be varied, and present a simple example of an experiment on the data.